code: def load_checkpoint(
      model, optimizer, output_dir, filename="checkpoint.pth.tar"
  ):
      file = os.path.join(output_dir, filename)
      if os.path.isfile(file):
          checkpoint = torch.load(file)
          start_epoch = checkpoint["epoch"]
          model.module.load_state_dict(checkpoint["state_dict"])
          optimizer.load_state_dict(checkpoint["optimizer"])
          print(
              "=> load checkpoint {} (epoch {})".format(
                  file, start_epoch
              )
          )
  
          return start_epoch, model, optimizer
  
      else:
          print("=> no checkpoint found at {}".format(file))
          return 0, model, optimizer
  
imports: import os
  import torch
  import torch.optim
  import numpy
  from sklearn.metrics import auc
label_desc: Use image node_id 15 for calling a global function with example usage: load_checkpoint(model, optimizer, output_dir, filename) and returns: start_epoch,  model,  optimizer, int,  model,  optimizer